Average correlations for individual pathways for ALL (blue) and prostate cancer (violet) are shown by horizontally dashed lines.

<p>The two curves correspond to the rank ordered correlation values for ALL (blue) and prostate cancer (violet). For ST I (green - ), ST II (orange - ), ST III (purple, ) and ST IV (brown - ) the projections of the range of correlation values is shown on the right-hand side.</p

Left column: prostate cancer. Right column: ALL. Power, false positive rate and number of significant pathways for GSEA (red), <i>sum of t-square</i> (blue) and Hotelling's (green).

<p>Left column: prostate cancer. Right column: ALL. Power, false positive rate and number of significant pathways for GSEA (red), <i>sum of t-square</i> (blue) and Hotelling's (green). </p

Left: Hotelling's , Middle: <i>sum of t-square</i>, Right: GSEA.

<p> The regression line is used to predict the <i>optimal</i> sample size (red cross) found from the intersection of the regression line with the horizontal dashed line corresponding to a ‘zero distance to convergence’.</p

Simulation type III (A) and IV (B) : Power, FPR and <i>number of significant pathways</i> for GSEA (red), <i>sum of t-square</i> (blue) and Hotelling's test (green).

<p>DC =  (light color), (medium color), (dark color). Simulated data are from the transcriptional regulatory network of yeast <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037510#pone.0037510-Balaji1" target="_blank">[50]</a>.</p

Simulation type III (A) and IV (B): Power, FPR and number of significant pathways for GSEA (red), <i>sum of t-square</i> (blue) and Hotelling's (green).

<p> DC =  (light color), (medium color), (dark color). Simulated data are from the protein network of yeast <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037510#pone.0037510-Breitkreutz1" target="_blank">[49]</a>.</p

Simulation type I (A, B) and II (C): Power, FPR and number of significant pathways for GSEA (red), <i>sum of t-square</i> (blue) and Hotelling's (green).

<p>DC =  (light color), (medium color), (dark color).</p

The optimal number of clusters for the three data-sets obtained by using consensus indices (CI).

<p>The optimal numbers of clusters (for three data-sets) for a clustering solution is represented by the set , where is the optimal number of clusters in the data.</p

The normalized mutual information, , between reference clusters, , and the number of clusters, , obtained by hierarchical clustering for three data-sets (left), (right) and (bottom). for each has been generated by sampling the data sets , where (data set ).

<p>The total number of descriptors equals 919. They belong to 6 different categories which are as follows: connectivity indices (24), edge adjacency indices (301), topological indices (57), walk path counts (28), information indices (40) and 2D Matrix-based (469).</p

Hierarchical clustering using the average algorithm, (left), (middle), (right).

<p>The total number of descriptors equals 919. They belong to 6 different categories which are as follows: connectivity indices (24), edge adjacency indices (301), topological indices (57), walk path counts (28), information indices (40) and 2D Matrix-based (469).</p

Consensus indices using the <i>adjusted rand index</i> for estimating the number of clusters in the data.

<p>These plots have been generated by sampling the data sets , where for the three data sets, (left), (right), (bottom). The dotted red line shows the optimal number of clusters.</p